Data Recommendation Engines

Recommendation System    |    Beginner
  • 13 Videos | 1h 10m 11s
  • Includes Assessment
  • Earns a Badge
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This 13-video course explores recommendation engines, systems which provide various users with items or products that they may be interested in by observing their previous purchasing, search, and behavior histories. They are used in many industries to help users find or explore products and content; for example, to find movies, news, insurance, and a myriad of other products and services. Learners will examine the three main types of recommendation systems: item-based, user-based or collaborative, and content-based. The course next examines how to collect data to be used for learning, training, and evaluation. You will learn how to use RStudio, an open-source IDE (integrated development environment) to import, filter, and massage data into data sets. Learners will create an R function that will give a score to an item based on other user ratings and similarity scores. You will learn to use R to create a function called compareUsers, to create an item-to-item similarity or content score. Finally, learn to validate and score by using the built-in R function RMSE (root mean square error).

WHAT YOU WILL LEARN

  • describe what a Recommendation Engine does, how it can be used, and the types and reasons they are used
    compare the different types of Recommendation Engines and how they can be used to solve different recommendation problems
    describe the process of collecting data and why data sets that can be used for learning, training, and evaluating a Recommendation Engine are needed
    use R to import, filter, and massage data into data sets
    describe how Similarity and Neighborhoods can be used to score users and items against another user or a new item
    create an R function that will score a user against another user to compare their similarity
  • create an R function that will give a score to an item a user has not seen before based on other users' ratings and similarity scores
    create an R function that finds similar users and finds products they liked which would be good to recommend to the user
    use R to create an Item to Item similarity, or content, score to Recommend similar items
    evaluate a Recommendation Engine by using known data and metrics to calculate the accuracy of recommendations
    validate and score a Recommendation System using R and an evaluation data set
    describe the types and interfaces required to build a Recommendation System

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 39s
    UP NEXT
  • Playable
    2. 
    Describing Recommendation Engines
    2m 55s
  • Locked
    3. 
    Comparing the Types of Recommendation Engines
    4m 17s
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    4. 
    Collecting and Manipulating Data
    4m 29s
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    5. 
    Manipulating Data in R
    6m 27s
  • Locked
    6. 
    Describing Similarity and Neighborhoods
    3m 22s
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    7. 
    Creating a Recommendation Engine
    5m 10s
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    8. 
    Recommending Another Item
    4m 57s
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    9. 
    Finding Items to Recommend
    5m 5s
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    10. 
    Recommending Items Based on Other Items
    7m 47s
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    11. 
    Evaluating a Recommendation System
    4m 54s
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    12. 
    Validating a Recommendation System
    6m 22s
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    13. 
    Exercise: Creating a Recommendation Engine
    7m 18s

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